How Does Object Detection Work?
Object detection is a computer vision task that involves identifying and locating objects within images or video streams. It combines two key processes: classification and localization. The aim is to not only detect the presence of an object but also to establish its position.
1. Data Collection
The first step involves collecting a large dataset of images containing the objects of interest. These images are annotated with bounding boxes that indicate the location of objects.
2. Preprocessing
Images are preprocessed to enhance features and standardize inputs. Common techniques include resizing, normalization, and data augmentation to improve model robustness.
3. Model Selection
Additionally, various algorithms can be implemented for object detection, such as:
- Convolutional Neural Networks (CNNs)
- Region-based CNN (R-CNN)
- YOLO (You Only Look Once)
- SSD (Single Shot MultiBox Detector)
4. Training the Model
The selected model is trained on annotated images. The training process adjusts the model's weights to minimize the difference between predicted and actual bounding boxes and class labels.
5. Inference
Once trained, the model can detect objects in new images. It outputs the predicted labels and bounding boxes, enabling applications like autonomous vehicles, facial recognition, and surveillance.
In summary, object detection leverages advanced machine learning techniques to identify, classify, and locate objects, playing a key role in various technological applications.